class_docstr: KNRM model.
  
  Examples:
      >>> model = KNRM()
      >>> model.params['embedding_input_dim'] =  10000
      >>> model.params['embedding_output_dim'] =  10
      >>> model.params['embedding_trainable'] = True
      >>> model.params['kernel_num'] = 11
      >>> model.params['sigma'] = 0.1
      >>> model.params['exact_sigma'] = 0.001
      >>> model.guess_and_fill_missing_params(verbose=0)
      >>> model.build()
docstr: Gaussian kernel layer in KNRM.
  
  :param mu: Float, mean of the kernel.
  :param sigma: Float, sigma of the kernel.
  :return: `keras.layers.Layer`.
docstr_tok: ["Gaussian","kernel","layer","in","KNRM",".",":","param","mu",":","Float",",","mean","of","the","kernel",".",":","param","sigma",":","Float",",","sigma","of","the","kernel",".",":","return",":","`","keras.layers.Layer","`","."]
code: def _kernel_layer(cls, mu: float, sigma: float) -> keras.layers.Layer:
      """
      Gaussian kernel layer in KNRM.
  
      :param mu: Float, mean of the kernel.
      :param sigma: Float, sigma of the kernel.
      :return: `keras.layers.Layer`.
      """
  
      def kernel(x):
          return tf.math.exp(-0.5 * (x - mu) * (x - mu) / sigma / sigma)
  
      return keras.layers.Activation(kernel)
  
file_path: MatchZoo/matchzoo/models/knrm.py
imports: import keras
  import tensorflow
  from matchzoo.engine.base_model import BaseModel
  from matchzoo.engine.param import Param
  from matchzoo.engine import hyper_spaces
label_desc: Use image node_id 3 for calling the KNRM obj's underlying member method code with example usage: obj._kernel_layer(cls, mu, sigma) and returns: keras, tf